Effects of conspecific density dependence on tree seedling recruitment and diversity in a temperate natural forest

Local conspecific competition is known to reduce plant performance and promote stable community diversity; however, the complex interactions between species and their environment, which can either amplify or mitigate this effect, remain poorly understood. Here, we present a decade-long demographic study tracking 15,852 seedlings from 14 tree species within 92 ha of permanent forest plots in northeastern China. Hierarchical Bayesian models were used to analyze the effects of neighborhood competition, neighborhood character (i.e., resources availability, and neighborhood functional variation) and their interactive effect on seedling recruitment and community diversity. We found that conspecific density dependence (CDD) reduced species recruitment but increased community diversity. The magnitude of CDD was significantly associated with interspecific variation in reproductive traits (i.e., seed mass and dispersal distance) and aboveground resource-use traits (i.e., leaf C:N ratio and specific leaf area), but not with belowground traits (i.e., specific root length and root C:N ratio). Importantly, greater shade tolerance dissimilarity (STD) between seedlings and neighbors directly enhanced recruitment and weakened negative CDD, while also amplifying the positive effect of CDD on community diversity, particularly under strong conspecific competition, high dissimilarity increased the inverse Simpson index by a median of 2.24 % (95 % CI: 0.68 %–3.85 %). Furthermore, while resource availability and competition acted independently on recruitment, increased light availability strengthened the positive relationship between CDD and community diversity, with the diversity gain from strong competition being more than double in high-light versus low-light environments. Our results underscore the critical role of maintaining trait diversity, alongside managing light availability, to enhance forest resilience and inform adaptive management under changing environmental conditions.